A model for transition-based visuospatial pattern recognition
Name
755089989-MIT.pdf
Description
Full printable version
Size
5.34 MB
Format
Adobe PDF
Checksum (MD5)
0c3b8affe4f8b27ce08e104c862e861a
Author(s)
Correa, Telmo Luis, Jr
Advisor(s)
Patrick H. Winston.
Date Issued
2011
Publisher
Massachusetts Institute of Technology
Abstract
In my research, I designed and implemented a system for learning and recognizing visual actions based on state transitions. I recorded three training videos of each of 16 actions (approach, bounce, carry, catch, collide, drop, fly over, follow, give, hit, jump, pick, push, put, take, throw), each lasting 10 seconds and 300 frames. After using a prototype system developed by Dr. Satyajit Rao for focus and actor recognition, actions are represented as qualitative state transitions, tied together to form tens of thousands of patterns, which are then available as action classifiers. The resulting system was able to build simple, intuitive classifiers that fit the training data perfectly.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 87).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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